Summer 2009 RLM 11.176 MTWTHF 10:00 - 11:30 a.m. June 4, 2009 – July 9, 2009
Course Description. A course in modern computationally-intensive statistical methods including simulation, optimization methods, Monte Carlo integration, maximum likelihood / EM parameter estimation, Markov chain Monte Carlo methods, resampling methods, non-parametric density estimation. Prerequisite: Graduate Standing and Mathematics 362K and 378K, or consent of instructor.
We will use the statistics package R, which is a free program so I expect that all students will download it to use. I will provide explanations of how to use these programs and most of the computer work in the course will be modifying code which is provided.
The grading in the course will be based on homework assignments and two take-home exams. Students may collaborate on the homework assignments, but the take-home exams must be done individually.
Textbook: Required: Computational Statistics
by Geof H. Givens and Jennifer A.Hoeting, Wiley, 2005.
Recommended:
Statistical Computing in R by Maria Rizzo, Chapman and Hall, 2007.
These books will be available on 2-hour reserve in the RLM library.
If you read the Preface of the textbook, you will see that the authors assume familiarity with several statistical techniques that are beyond the level of the prerequisite for this course. We will not cover chapters that use all of these, and for those we do cover, I expect to teach those statistical topics as well as the computational topics. In particular, students should already be familiar with maximum likelihood estimation and regression. Other topics will be introduced in the course as needed.
There is a suggestion in the Preface for the chapters/topics to be covered in a one-semester course. This seems too ambitious to me for this particular class, so I believe we will follow a "more leisurely pace" as they say in the Preface. It will be a small class and it will be a discussion / lecture class more than mainly a lecture class.